R

library(dplyr)
# Keep only 2 columns
select(porgs, id, age)
# Drop the mass column
select(porgs, -mass)
# Put the age column first, but
# keep everything else the same
select(porgs, age, everything())

# Sort by age w/ YOUNGEST on top
arrange(porgs, age)
# Sort by age w/ ELDEST on top
arrange(porgs, desc(age))
# Sort by color and then by age
arrange(porgs, color, desc(age))

left_join() keeps all rows and columns in the left table, but only keeps rows in the right table that match.

# Table w/ porg ages and heights
porgs

# Table w/ porg names
porg_names

# Join together by id columns
together <- left_join(porgs, 
                      porg_names, 
                      by = "id")

library(readr)
# Save data to a CSV text file
write_csv(porgs, "my_porg_data.csv")

library(ggsave)
# Save the last plot you viewed
ggsave("2019_porg_plot.png")
# Save an earlier named plot
ggsave(best_plot, "the_best_plot.png")

Use ifelse() to create new values that depend on the value of another column. For example, to only label the porgs with a height over 60cm as “tall”.

# If a porg's height is > 60cm label it as "tall", 
# otherwise label as "short"
mutate(porgs, label = ifelse(height > 60, "tall", "short"))